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检索条件"任意字段=6th Asia Conference on Machine Learning and Computing, ACMLC 2024"

共 606 条记录，以下是71-80 订阅

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Ensemble machine learning Method for Health Insurance Premium Prediction 6

Ensemble Machine Learning Method for Health Insurance Premiu...

引用

6th IEEE International conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Sijie, Lee Sia, Florence Alfred, Rayner Moung, Ervin Gubin Universiti Malaysia Sabah Creative Advanced Machine Intelligence Research Center Faculty of Computing and Informatic Malaysia

ISBN: (纸本)9798350389692

the insurance industry remains as one of the most challenging sectors in the business world as the pricing issues are difficult to tackle due to inflation, compliance with regulatory requirements, customer perceptions and purchasing behavior as well as competition from other rival companies. Consequently, pricing has become a complex task for the insurance companies as they struggle to determine the suitable price range of health insurance premium that can be affordable by everyone. machine learning methods have been widely used to predict the premium of health insurance in order to provide a highly accurate pricing. However, there are various ensemble machine learning methods that have not been exploited for the health insurance premium prediction. this paper aims to investigate the performance of ensemble method that integrates Artificial Neural Network with another machine learning model through the stacking technique to forecast the amount of premium charged for health insurance. the experiment results show that the ensemble model has the capability to outperform the traditional single machine learning model in predicting the health insurance premium on the US Health Insurance dataset. © 2024 IEEE.

关键词： Adversarial machine learning

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Harnessing Deep learning for Fault Detection in Industry 4.0: A Multimodal Approach 6

Harnessing Deep Learning for Fault Detection in Industry 4.0...

引用

6th International conference on Cognitive machine Intelligence

作者： Shen, Jialie Morrison, Marie Miao, Haiyan Gu, Fengshou City St Georgias Univ London Dept Comp Sci London England Univ Bristol Sch Engn Math & Technol Bristol Avon England Univ Huddersfield Sch Comp & Engn Huddersfield W Yorkshire England

ISBN: (纸本)9798350386738;9798350386721

the advent of Industry 4.0 has reshaped the modern industries (e.g., Manufacturing, Automotive, Aerospace and Defense), driven by the rapid development of artificial intelligence, smart sensing technologies, and interconnected cyber-physical systems. One of the most important goal for Industry 4.0 is the improvement and enhancement in productivity, reduction of production losses, and operational efficiency. High quality fault detection within different environments is the foundation to achieve these goals. Industry 4.0 emphasizes the use of interconnected systems, smart sensors, and advanced analytics, which generate large volumes of data from production environments. the data complexity and scale, product by the widespread deployment of sensors and IoT devices, demands sophisticated machine learning (ML) and data-driven methods. In this paper, we argue that the future of accurate and reliable fault detection lies in the seamless fusion of edge-cloud computing, explainable AI, and adaptive machine learning algorithms capable of processing high-frequency, multimodal data streams in real time. Different kinds of advantages for multimondal learning and core enabling techniques are also discussed and revise. this vision paper outlines the future trajectory of multimodal learning based fault detection, via identifying critical research gaps and promising directions for future study.

关键词： Deep learning Fault Detection Multimodal learning Industry 4.0

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A 3D System for Generating Z-Direction Multiple Microscopic Images Based on Deep learning 6

A 3D System for Generating Z-Direction Multiple Microscopic ...

引用

6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Wang, Yufei School of Optoelectronic Science and Engineering Uestc Chengdu China

ISBN: (纸本)9798350374315

With the development of artificial intelligence technology, people have begun to study methods based on deep learning to reconstruct the geometric morphology of three-dimensional objects. In the early stage of the project, we first discussed obtaining the height information of various points on the surface of the object through clustering evaluation algorithm, and then reconstructing the point cloud representation of the object. Based on deep learning methods, we completed the surface reconstruction of the object, but this method did not achieve the expected experimental results. this article proposes a method based on deep learning that combines multi focus image fusion and depth estimation. Firstly, a multi focus image fusion algorithm is used to preprocess the collected microscopic images to obtain a globally clear image. then, depth estimation is performed on this clear image to obtain the height information of the object surface, and three-dimensional point cloud data is reconstructed. Finally, we also explored the three-dimensional reconstruction of cell microscopy images using transmissive lighting. © 2024 IEEE.

关键词： Adversarial machine learning

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User Portrait Construction of Diet therapy Culture Based on machine learning 6

User Portrait Construction of Diet Therapy Culture Based on ...

引用

6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Dong, Jie Fan, Siyu Men, Rui School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Shenyang China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Shenyang China

ISBN: (纸本)9798350374315

With the development of high-quality economy, modern people are facing various health challenges while their living standards are improving, the risk of disease is rising, and the need for health is urgent and growing. Traditional Chinese medicine food therapy culture is in line with the pursuit of healthy lifestyle in modern society, and is favored by more and more people. In this paper, questionnaires were distributed online and offline for investigation, K-means algorithm was used to cluster and build user portraits, population needs with different individual characteristics were subdivided, and decision tree regression algorithm was used to predict user dietary therapy behavior. the results show that the analysis of dietary therapy users according to the above method is accurate, and the fine division of users is conducive to the characteristic development of dietary therapy culture. © 2024 IEEE.

关键词： Adversarial machine learning

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A Time Series Forecasting Model Based on machine learning 6

A Time Series Forecasting Model Based on Machine Learning

引用

6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Cui, Xiangyu School of Ai and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou215018 China

ISBN: (纸本)9798350374315

Time series forecasting plays a critical role in domains such as finance, environmental science, and health management. Traditional methods like ARIMA and exponential smoothing often fall short in addressing the complexities of modern datasets, especially in modeling non-linear relationships and intricate dependencies. this study introduces the CLT model, an innovative ensemble approach that combines CatBoost, LightGBM, and Transformers to enhance forecasting accuracy and reliability. Employing a dataset of Bitcoin prices from 2014 to 2024, this research involves rigorous data preprocessing, sophisticated feature engineering, and systematic model selection. the CLT model significantly outperforms traditional and single-model approaches by effectively capturing complex dependencies and demonstrating remarkable stability across various validation folds. the results demonstrate that the CLT model outperforms traditional and single-model approaches by effectively capturing complex dependencies and showing remarkable stability across various validation folds. In terms of quantitative metrics, our model achieves an average Root Mean Square Error (RMSE) of 434.54 and an average Mean Absolute Error (MAE) of 374.9, underscoring its superior predictive capabilities. this research underscores the potential of integrating multiple advanced machine learning models to significantly improve the performance of time series forecasting, offering a scalable and efficient solution for high-stakes domains. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Mobile Application for Personalized Hair Care Using Advanced machine learning Techniques 6

A Novel Mobile Application for Personalized Hair Care Using ...

引用

6th International conference on Advancements in computing, ICAC 2024

作者： Kiriella, Anjali Rathnayake, Samadhi thilakarthna, thusithanjana Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

Recent advancements in computer vision and machine learning have enabled personalized hair care, advancing beyond traditional methods by catering to individual hair types and health. this paper presents Glowllp, a mobile application that provides personalized hair care recommendations through three primary modules: image quality assessment, hair type detection, and health prediction. the image quality assessment module employs edge detection and SVM classifiers to filter blurry images, achieving over 95% accuracy. For hair type classification, a hybrid model of Mask R-CNN and Vision Transformers (ViTs) identifies hair types with 92% accuracy. the health assessment module predicts hair loss and other conditions using Random Forest and XGBoost models with an F1 score of 0.91, while K-means clustering further tailor's recommendations. By integrating these modules, Glow Up delivers a comprehensive, user-friendly solution that enhances hair care personalization. © 2024 IEEE.

关键词： K-means clustering

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Opinion Mining System for Influence Detection Using machine learning to Secure Business Reputation 6th

Opinion Mining System for Influence Detection Using Machine ...

引用

6th International conference on Soft computing and Data Mining (SCDM)

作者： Ismail, Shahrinaz Khant, Kyi Lin Asia Pacific Univ Technol & Innovat APU Kuala Lumpur Malaysia Huazhong Univ Sci & Technol HUST Wuhan Hubei Peoples R China

ISBN: (纸本)9783031669644;9783031669651

the use of social media becomes a must for almost all businesses, including micro and small enterprises. Despite the benefits of social media marketing, the feedbacks and responses retrieved from audience or customers via posts and comments in social media could be a challenge. Comments received could be negative in nature, which could affect the businesses in a negative way. the negative influence could jeopardize the business reputation, especially if the comments are highly negative and go viral. In sustaining their businesses, the micro and small business owners should not be left behind in using the latest analysis method in securing their business reputation. this paper proposes a simple website that makes use of the back-end sentiment analysis, to detect negative influence on their products and businesses. the results are presented in the form of process flow and interface design of the proposed website, made simple for the target users.

关键词： Opinion Mining Sentiment Analysis machine learning Influence Detection Securing Reputation

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machine learning Based Approaches For Android Malware Detection using Hybrid Feature Analysis 6

Machine Learning Based Approaches For Android Malware Detect...

引用

6th International conference on computing and Informatics, ICCI 2024

作者： Neil, Ahmed M. Shabaan, Eman El Qout, Mervat Emara, Karim Computer Systems Department Ain shams University Cairo Egypt

ISBN: (纸本)9798350373875

Smart Phones have become an integral part of our lives, offering a wide range of functionalities and services. However, as the popularity of Android has grown, so has the threat of malware attacks targeting these devices. the increasing number of mobile applications and the significant amount of personal and sensitive information stored on smartphones make them lucrative targets for malicious actors. It is crucial to develop effective methods to identify and mitigate these threats to ensure the security and privacy of Android users. this paper presents a comparitive study of different machine learning approaches for binary classification (malicious and benign) of Android applications, based on Hybrid feature analysis (static and dynamic). Also it conducts a comparative analysis of the highest results to other relevant studies. Experimental evaluations results reach 98% accuracy in detecting malicious applications. © 2024 IEEE.

关键词： Android malware

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An Integrated machine learning and Deep learning Framework for Credit Card Approval Prediction 6

An Integrated Machine Learning and Deep Learning Framework f...

引用

6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Tong, Kejian Han, Zonglin Shen, Yanxin Long, Yujian Wei, Yijing Mukilteo United States University of California San Diego La Jolla United States Hangzhou China Frisco United States Northwestern University Evanston United States

ISBN: (纸本)9798350374315

Credit scoring is vital in the financial industry, assessing the risk of lending to credit card applicants. Traditional credit scoring methods face challenges with large datasets and data imbalance between creditworthy and non-creditworthy applicants. this paper introduces an advanced machine learning and deep learning framework to improve the accuracy and reliability of credit card approval predictions. We utilized extensive datasets of user application records and credit history, implementing a comprehensive preprocessing strategy, feature engineering, and model integration. Our methodology combines neural networks with an ensemble of base models, including logistic regression, support vector machines, k-nearest neighbors, decision trees, random forests, and gradient boosting. the ensemble approach addresses data imbalance using Synthetic Minority Over-sampling Technique (SMOTE) and mitigates overfitting risks. Experimental results show that our integrated model surpasses traditional single-model approaches in precision, recall, F1-score, AUC, and Kappa, providing a robust and scalable solution for credit card approval predictions. this research underscores the potential of advanced machine learning techniques to transform credit risk assessment and financial decisionmaking. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring Cough Sounds in Chronic Obstructive Pulmonary Disease (COPD) Patients Using machine learning Techniques 6

Exploring Cough Sounds in Chronic Obstructive Pulmonary Dise...

引用

6th IEEE Eurasia conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Hung, Shu Hui Tseng, Tzu-I. National Center for High-performance Computing National Applied Research Laboratories Taichung Taiwan National Center for High-performance Computing National Applied Research Laboratories Hinchu Taiwan

ISBN: (纸本)9798350396133

We classified respiratory sounds that indicate the early stages of coughing in patients with chronic obstructive pulmonary disease (COPD). Deep learning techniques with a voice spectrogram were utilized for respiratory sound detection. Convolutional neural networks (CNNs) were applied to classify COPD by extracting features from the respiratory sounds. the optimized model achieved an accuracy rate of 91 %. Since the pretraining database was sourced from publicly available data from the Internet, the model was enhanced by incorporating COPD sounds provided by doctors as feedback. © 2024 IEEE.

关键词： Lung cancer

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